Along with the development of high-throughput sequencing technologies, both sample size and SNP number are increasing rapidly in genome-wide association studies (GWAS), and the associated computation is more challenging than ever. Here, we present a memory-efficient, visualization-enhanced, and parallel-accelerated R package called “rMVP” to address the need for improved GWAS computation. rMVP can 1) effectively process large GWAS data, 2) rapidly evaluate population structure, 3) efficiently estimate variance components by Efficient Mixed-Model Association eXpedited (EMMAX), Factored Spectrally Transformed Linear Mixed Models (FaST-LMM), and Haseman-Elston (HE) regression algorithms, 4) implement parallel-accelerated association tests of markers using general linear model (GLM), mixed linear model (MLM), and fixed and random model circulating probability unification (FarmCPU) methods, 5) compute fast with a globally efficient design in the GWAS processes, and 6) generate various visualizations of GWAS-related information. Accelerated by block matrix multiplication strategy and multiple threads, the association test methods embedded in rMVP are significantly faster than PLINK, GEMMA, and FarmCPU_pkg. rMVP is freely available at https://github.com/xiaolei-lab/rMVP.

Background: Obese and lean pig breeds show obvious differences in muscle growth; however, the molecular mechanism underlying phenotype variation remains unknown. Prenatal muscle development programs postnatal performance. Here, we describe a genome-wide analysis of differences in prenatal skeletal muscle between Tongcheng (a typical indigenous Chinese breed) and Landrace (a leaner Western breed) pigs. Results: We generated transcriptome profiles of skeletal muscle from Tongcheng and Landrace pigs at 33, 65 and 90 days post coitus (dpc), using long serial analysis of gene expression (LongSAGE). We sequenced 317,115 LongSAGE tags and identified 1,400 and 1,201 differentially expressed transcripts during myogenesis in Tongcheng and Landrace pigs, respectively. From these, the Gene Ontology processes and expression patterns of these differentially expressed genes were constructed. Most of the genes showed different expression patterns in the two breeds. We also identified 532, 653 and 459 transcripts at 33, 65 and 90 dpc, respectively, that were differentially expressed between the two breeds. Growth factors, anti-apoptotic factors and genes involved in the regulation of protein synthesis were up-regulated in Landrace pigs. Finally, 12 differentially expressed genes were validated by quantitative PCR. Conclusion: Our data show that gene expression phenotypes differ significantly between the two breeds. In particular, a slower muscle growth rate and more complicated molecular changes were found in Tongcheng pigs, while genes responsible for increased cellular growth and myoblast survival were up-regulated in Landrace pigs. Our analyses will assist in the identification of candidate genes for meat production traits and elucidation of the development of prenatal skeletal muscle in mammals.

Background: Haemophilus parasuis(HPS) is an important swine pathogen that causes Glässer's disease, which is characterized by fibrinous polyserositis, meningitis and arthritis. The molecular mechanisms that underlie the pathogenesis of the disease remain poorly understood, particularly the resistance of porcine immune system to HPS invasion. In this study, we investigated the global changes in gene expression in the spleen following HPS infection using the Affymetrix Porcine Genechip™.Results: A total of 931 differentially expressed (DE) transcripts were identified in the porcine spleen 7 days after HPS infection; of these, 92 unique genes showed differential expression patterns based on analysis using BLASTX and Gene Ontology. The DE genes involved in the immune response included genes for inflammasomes (RETN,S100A8,S100A9,S100A12), adhesion molecules (CLDN3,CSPG2,CD44,LGALS8), transcription factors (ZBTB16,SLC39A14,CEBPD,CEBPB), acute-phase proteins and complement (SAA1,LTF,HP,C3), differentiation genes for epithelial cells and keratinocytes (TGM1,MS4A8B,CSTA), and genes related to antigen processing and presentation (HLA-B,HLA-DRB1). Further immunostimulation analyses indicated that mRNA levels ofS100A8,S100A9, andS100A12in porcine PK-15 cells increased within 48 h and were sustained after administration of lipopolysaccharide (LPS) and Poly(I:C) respectively. In addition, mapping of DE genes to porcine health traits QTL regions showed that 70 genes were distributed in 7 different known porcine QTL regions. Finally, 10 DE genes were validated by quantitative PCR.Conclusion: Our findings demonstrate previously unrecognized changes in gene transcription that are associated with HPS infectionin vivo, and many potential cascades identified in the study clearly merit further investigation. Our data provide new clues to the nature of the immune response in mammals, and we have identified candidate genes that are related to resistance to HPS.

Along with the development of high-throughout sequencing technologies, both sample size and number of SNPs are increasing rapidly in Genome-Wide Association Studies (GWAS) and the associated computation is more challenging than ever. Here we present a Memory-efficient, Visualization-enhanced, and Parallel-accelerated R package called “rMVP” to address the need for improved GWAS computation. rMVP can: (1) effectively process large GWAS data; (2) rapidly evaluate population structure; (3) efficiently estimate variance components by EMMAX, FaST-LMM, and HE regression algorithms; (4) implement parallel-accelerated association tests of markers using GLM, MLM, and FarmCPU methods; (5) compute fast with a globally efficient design in the GWAS processes; and (6) generate various visualizations of GWAS related information. Accelerated by block matrix multiplication strategy and multiple threads, the association test methods embedded in rMVP are approximately 5-20 times faster than PLINK, GEMMA, and FarmCPU_pkg. rMVP is freely available at https://github.com/xiaolei-lab/rMVP .

Hematologic and biochemical data are useful for indicating disease diagnosis and growth performance in swine. However, the assessment of these parameters in healthy commercial pigs is rare in China. Thus, blood samples were collected from 107 nursery pigs and 87 sows and were analyzed for 25 hematologic and 14 biochemical variables. After the rejection of the outliers and the detection of the data distribution, the correlations between the blood parameters were analyzed and the hematologic/biochemical RIs were preliminarily established using the 95% percentile RI. Correlation analysis showed that albumin was the hub parameter among the blood parameters investigated, and genes overlapping with key correlated variables were discovered. Most of the hematologic and biochemical parameters were significantly different between nursery pigs and sows. The 95% RIs of white blood cells and red blood cells were 7.18–24.52 × 10⁹/L and 5.62–7.84 × 10¹²/L, respectively, for nursery pigs, but 9.34–23.84 × 10⁹/L and 4.98–8.29 × 10¹²/L for sows. The 95% RIs of total protein and albumin were 43.16–61.23 g/dL and 19.35–37.86 g/dL, respectively, for nursery pigs, but 64.96–88.68 g/dL and 31.91–43.28 g/dL for sows. In conclusion, our study highlights the variability in blood parameters between nursery pigs and sows and provides fundamental data for the health monitoring of commercial pigs in China.

Litter size in pigs is a major factor affecting the profitability in the pig industry. The peri-implantation window in pigs is characterized by the coordinated interactions between the maternal uterine endometrium and the rapidly elongating conceptuses and represents a period of time during which a large percentage of the developing conceptuses are lost. However, the gene expression and regulatory networks in the endometrium contributing to the establishment of the maternal: placental interface remain poorly understood.